Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, Romania.
Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
Sensors (Basel). 2021 May 21;21(11):3580. doi: 10.3390/s21113580.
This paper presents different prediction methods based on decision tree and ensemble learning to suggest possible next assembly steps. The predictor is designed to be a component of a sensor-based assembly assistance system whose goal is to provide support via adaptive instructions, considering the assembly progress and, in the future, the estimation of user emotions during training. The assembly assistance station supports inexperienced manufacturing workers, but it can be useful in assisting experienced workers, too. The proposed predictors are evaluated on the data collected in experiments involving both trainees and manufacturing workers, as well as on a mixed dataset, and are compared with other existing predictors. The novelty of the paper is the decision tree-based prediction of the assembly states, in contrast with the previous algorithms which are stochastic-based or neural. The results show that ensemble learning with decision tree components is best suited for adaptive assembly support systems.
本文提出了基于决策树和集成学习的不同预测方法，以建议可能的下一个装配步骤。该预测器旨在成为基于传感器的装配辅助系统的一个组成部分，其目标是通过自适应指令提供支持，考虑到装配进度，以及未来在训练过程中估计用户的情绪。装配辅助站为没有经验的制造工人提供支持，但对有经验的工人也很有用。所提出的预测器在涉及学徒和制造工人的实验以及混合数据集上收集的数据进行了评估，并与其他现有预测器进行了比较。本文的新颖之处在于基于决策树的装配状态预测，与之前基于随机或神经的算法形成对比。结果表明，具有决策树组件的集成学习最适合自适应装配支持系统。
